Jiangzhuo Chen

EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems

Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems. EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results. EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.

Minimum energy accumulative routing in wireless networks

In this paper, we propose to address the energy efficient routing problem in multi-hop wireless networks with accumulative relay. In the accumulative relay model, partially overheard signals of previous transmissions for the same packet are used to decode it using a maximal ratio combiner technique [J.G. Proakis, 2001]. Therefore, additional energy saving can be achieved over traditional energy efficient routing. The idea of accumulative relay originates from the study of relay channel in information theory with a main focus on network capacity. It has been independently applied to minimum-energy broadcasting in L.G. Manish Agrawal et al. (2004), I. Maric and R. Yates (2002). We formulate the minimum energy accumulative routing problem (MEAR) and study it. We obtain hardness of approximation results counterbalanced with good heuristic solutions which we validate using simulations. Without energy accumulation, the classic shortest path (SP) algorithm finds the minimum energy path for a source-destination pair. However, we show that with energy accumulation, the SP can be arbitrarily bad. We turn our attention to heuristics and show that any optimal solution of MEAR can be converted to a canonical form - wave path. Armed with this insight, we develop a polynomial time heuristic to efficiently search over the space of all wavepaths. Simulation results show that our heuristic can provide more than 30% energy saving over minimum energy routing without accumulative relay. We also discuss the implementation issues of such a scheme.

Indemics: an interactive data intensive framework for high performance epidemic simulation

To respond to the serious threat of pandemics (e.g. 2009 H1N1 influenza) to human society, we developed Indemics (<u>In</u>teractive Epi<u>demic</u> <u>S</u>imulation), an interactive, data intensive, high performance modeling environment for realtime pandemic planning, situation assessment, and course of action analysis. Indemics was built upon a model of interactive data intensive scientific computation, supporting online interactions between users and simulations and enabling epidemic simulations over detailed social contact networks and realistic representations of complex public policies and intervention strategies. Instead of simply making a highly optimized parallel application run even faster, Indemics introduced several innovative ideas such as online interactive computation and HPC-DBMS integration that significantly improved the functionality, flexibility, modularity, and usability of HPC software. Our performance evaluation suggests that additional computational overhead incurred by Indemics compared to non-interactive simulations is easily offset by its new capabilities. Preliminary results show that Indemics significantly broadens the range of course of action scenarios that can be simulated and enables domain experts to analyze problems that were previously not possible to study.

Sensitivity of Household Transmission to Household Contact Structure and Size

Objective Study the influence of household contact structure on the spread of an influenza-like illness. Examine whether changes to in-home care giving arrangements can significantly affect the household transmission counts. Method We simulate two different behaviors for the symptomatic person; either s/he remains at home in contact with everyone else in the household or s/he remains at home in contact with only the primary caregiver in the household. The two different cases are referred to as full mixing and single caregiver, respectively. Results The results show that the household’s cumulative transmission count is lower in case of a single caregiver configuration than in the full mixing case. The household transmissions vary almost linearly with the household size in both single caregiver and full mixing cases. However the difference in household transmissions due to the difference in household structure grows with the household size especially in case of moderate flu. Conclusions These results suggest that details about human behavior and household structure do matter in epidemiological models. The policy of home isolation of the sick has significant effect on the household transmission count depending upon the household size.

I ndemics : An interactive high-performance computing framework for data-intensive epidemic modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented.

Coevolution of epidemics, social networks, and individual behavior: a case study

This research shows how a limited supply of antivirals can be distributed optimally between the hospitals and the market so that the attack rate is minimized and enough revenue is generated to recover the cost of the antivirals. Results using an individual based model find that prevalence elastic demand behavior delays the epidemic and change in the social contact network induced by isolation reduces the peak of the epidemic significantly. A microeconomic analysis methodology combining behavioral economics and agent-based simulation is a major contribution of this work. In this paper we apply this methodology to analyze the fairness of the stockpile distribution, and the response of human behavior to disease prevalence level and its interaction with the market.

Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza

This research compares the performance of bottom-up, self-motivated behavioral interventions with top-down interventions targeted at controlling an “Influenza-like-illness”. Both types of interventions use a variant of the ring strategy. In the first case, when the fraction of a persons direct contacts who are diagnosed exceeds a threshold, that person decides to seek prophylaxis, e.g. vaccine or antivirals; in the second case, we consider two intervention protocols, denoted Block and School: when a fraction of people who are diagnosed in a Census Block (resp., School) exceeds the threshold, prophylax the entire Block (resp., School). Results show that the bottom-up strategy outperforms the top-down strategies under our parameter settings. Even in situations where the Block strategy reduces the overall attack rate well, it incurs a much higher cost. These findings lend credence to the notion that if people used antivirals effectively, making them available quickly on demand to private citizens could be a very effective way to control an outbreak.

Interactions among human behavior, social networks, and societal infrastructures: A Case Study in Computational Epidemiology

Human behavior, social networks, and the civil infrastructures are closely intertwined. Understanding their co-evolution is critical for designing public policies and decision support for disaster planning. For example, human behaviors and day to day activities of individuals create dense social interactions that are characteristic of modern urban societies. These dense social networks provide a perfect fabric for fast, uncontrolled disease propagation. Conversely, people’s behavior in response to public policies and their perception of how the crisis is unfolding as a result of disease outbreak can dramatically alter the normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. In this chapter, we describe a computer simulation based approach to study these issues using public health and computational epidemiology as an illustrative example. We also formulate game-theoretic and stochastic optimization problems that capture many of the problems that we study empirically.

Almost) tight bounds and existence theorems for confluent flows

A flow is said to be confluent if at any node all the flow leaves along a single edge. Given a directed graph G with k sinks and non-negative demands on all the nodes of G, we consider the problem of determining a confluent flow that routes every node demand to some sink such that the maximum congestion at a sink is minimized. Confluent flows arise in a variety of application areas, most notably in networking; in fact, most flows in the Internet are confluent since Internet routing is destination based.We present near-tight approximation algorithms, hardness results, and existence theorems for confluent flows. The main result of this paper is a polynomial-time algorithm for determining a confluent flow with congestion at most 1 + ln(k) in G, if G admits a splittable flow with congestion at most 1. We complement this result in two directions. First, we present a graph G that admits a splittable flow with congestion at most 1, yet no confluent flow with congestion smaller than Hk, thus establishing tight upper and lower bounds to within an additive constant less than 1. Second, we show that it is NP-hard to approximate the congestion of an optimal confluent flow to within a factor of (lg k)/2, thus resolving the polynomial-time approximability to within a multiplicative constant. We also consider a demand maximization version of the problem. We show that if G admits a splittable flow of congestion at most 1, then a variant of the congestion minimization algorithm yields a confluent flow in G with congestion at most 1 that satisfies 1/3 fraction of total demand.We show that the gap between confluent flows and splittable flows is much smaller, if the underlying graph were k connected. In particular, we prove that k-connected graphs with k sinks admit confluent flows of congestion less than C + dmax, where C is the congestion of the best splittable flow, and dmax is the maximum demand of any node in G. The proof of this existence theorem is non-constructive and relies on topological techniques introduced in [16].

The effect of demographic and spatial variability on epidemics: A comparison between Beijing, Delhi, and Los Angeles

A social network is a critical infrastructure for the propagation of an infectious disease in a population. It is important to study the structural properties of the social network for identifying feasible public health interventions that can effectively contain a potential epidemic outbreak. In this work, we focus on flu-like diseases and corresponding people-people social contact networks. We study such social infrastructures of three cities: Los Angeles, USA, Beijing, China and Delhi, India. These contact networks are different due to different construction methodologies and the fact that the populations inherently have very different demographic structures and activity patterns. We compare them in terms of static structural properties (such as clustering coefficient, degree distribution), as well as disease dynamics and efficacy of intervention (e.g., school closure). The comparison between synthetic populations and social contact networks from different regions of the world can provide valuable insight on creating a global synthetic population and social infrastructure for studying public health problems.